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A DVD rental company needs your help! They want to figure out how many days a customer will rent a DVD for based on some features and has approached you for help. They want you to try out some regression models which will help predict the number of days a customer will rent a DVD for. The company wants a model which yeilds a MSE of 3 or less on a test set. The model you make will help the company become more efficient inventory planning.

The data they provided is in the csv file rental_info.csv. It has the following features:

  • "rental_date": The date (and time) the customer rents the DVD.
  • "return_date": The date (and time) the customer returns the DVD.
  • "amount": The amount paid by the customer for renting the DVD.
  • "amount_2": The square of "amount".
  • "rental_rate": The rate at which the DVD is rented for.
  • "rental_rate_2": The square of "rental_rate".
  • "release_year": The year the movie being rented was released.
  • "length": Lenght of the movie being rented, in minuites.
  • "length_2": The square of "length".
  • "replacement_cost": The amount it will cost the company to replace the DVD.
  • "special_features": Any special features, for example trailers/deleted scenes that the DVD also has.
  • "NC-17", "PG", "PG-13", "R": These columns are dummy variables of the rating of the movie. It takes the value 1 if the move is rated as the column name and 0 otherwise. For your convinience, the reference dummy has already been dropped.
import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Import any additional modules and start coding below
from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV
# getting the number of rental days

# read in data
rental_info = pd.read_csv('rental_info.csv')

# create length column
rental_info['rental_length'] = pd.to_datetime(rental_info['return_date']) - pd.to_datetime(rental_info['rental_date'])

# create rental length days column
rental_info['rental_length_days'] = rental_info['rental_length'].dt.days
# creating dummy variables
rental_info['deleted_scenes'] = np.where(rental_info['special_features'].str.contains('Deleted Scenes'), 1, 0)
rental_info['behind_the_scenes'] = np.where(rental_info['special_features'].str.contains('Behind the Scenes'), 1, 0)
# splitting data

# creating data 
cols_to_drop = ['special_features', 'rental_length', 'rental_length_days', 'rental_date', 'return_date']

X = rental_info.drop(cols_to_drop, axis=1)
y = rental_info['rental_length_days']

# split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=9)
# performing feature selection
# create lasso
lasso = Lasso(alpha=0.3, random_state=9)

# fit the model
lasso.fit(X_train, y_train)
lasso_coef = lasso.coef_
lasso_coef

# selecting features
X_train_lasso, X_test_lasso = X_train.iloc[:, lasso_coef > 0], X_test.iloc[:, lasso_coef > 0]
# linear regression
lr = LinearRegression()
lr.fit(X_train_lasso, y_train)
y_pred = lr.predict(X_test_lasso)
lr_mse = mean_squared_error(y_test, y_pred)
# random forest
# instantiate mode
rf = RandomForestRegressor()

# parameters
param_dist = {'n_estimators': np.arange(1,101,1),
              'max_depth': np.arange(1,11,1)}

# find best parameters
rand_search = RandomizedSearchCV(rf, param_distributions=param_dist, cv=5, random_state=9)

# fit object
rand_search.fit(X_train, y_train)

hyper_params = rand_search.best_params_

# new rf model
rf = RandomForestRegressor(n_estimators=hyper_params['n_estimators'], max_depth=hyper_params['max_depth'], random_state=9)

rf.fit(X_train, y_train)
rf_preds = rf.predict(X_test)
rf_mse = mean_squared_error(y_test, rf_preds)
# compare mse
print('Linear Regression MSE: ', lr_mse)
print('Random Forest MSE: ', rf_mse)

best_model = rf
best_mse = rf_mse